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Hypothetico-deductive model

April 26, 2024

The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions.[1]

Example edit

Main article: Scientific method

One example of an algorithmic statement of the hypothetico-deductive method is as follows:[2]

1. Use your experience: Consider the problem and try to make sense of it. Gather data and look for previous explanations. If this is a new problem to you, then move to step 2.
2. Form a conjecture (hypothesis): When nothing else is yet known, try to state an explanation, to someone else, or to your notebook.
3. Deduce predictions from the hypothesis: if you assume 2 is true, what consequences follow?
4. Test (or experiment): Look for evidence (observations) that conflict with these predictions in order to disprove 2. It is a fallacy or error in one's reasoning to seek  3 directly as proof of 2. This formal fallacy is called affirming the consequent.[3]

One possible sequence in this model would be 1, 2, 3, 4. If the outcome of 4 holds, and 3 is not yet disproven, you may continue with 3, 4, 1, and so forth; but if the outcome of 4 shows 3 to be false, you will have to go back to 2 and try to invent a new 2, deduce a new 3, look for 4, and so forth.

Note that this method can never absolutely verify (prove the truth of) 2. It can only falsify 2.[4] (This is what Einstein meant when he said, "No amount of experimentation can ever prove me right; a single experiment can prove me wrong."[5])

Discussion edit

Additionally, as pointed out by Carl Hempel (1905–1997), this simple view of the scientific method is incomplete; a conjecture can also incorporate probabilities, e.g., the drug is effective about 70% of the time.[6] Tests, in this case, must be repeated to substantiate the conjecture (in particular, the probabilities). In this and other cases, we can quantify a probability for our confidence in the conjecture itself and then apply a Bayesian analysis, with each experimental result shifting the probability either up or down. Bayes' theorem shows that the probability will never reach exactly 0 or 100% (no absolute certainty in either direction), but it can still get very close to either extreme. See also confirmation holism.

Qualification of corroborating evidence is sometimes raised as philosophically problematic. The raven paradox is a famous example. The hypothesis that 'all ravens are black' would appear to be corroborated by observations of only black ravens. However, 'all ravens are black' is logically equivalent to 'all non-black things are non-ravens' (this is the contrapositive form of the original implication). 'This is a green tree' is an observation of a non-black thing that is a non-raven and therefore corroborates 'all non-black things are non-ravens'. It appears to follow that the observation 'this is a green tree' is corroborating evidence for the hypothesis 'all ravens are black'. Attempted resolutions may distinguish:

  • non-falsifying observations as to strong, moderate, or weak corroborations
  • investigations that do or do not provide a potentially falsifying test of the hypothesis.[7]

Evidence contrary to a hypothesis is itself philosophically problematic. Such evidence is called a falsification of the hypothesis. However, under the theory of confirmation holism it is always possible to save a given hypothesis from falsification. This is so because any falsifying observation is embedded in a theoretical background, which can be modified in order to save the hypothesis. Karl Popper acknowledged this but maintained that a critical approach respecting methodological rules that avoided such immunizing stratagems is conducive to the progress of science.[8]

Physicist Sean Carroll claims the model ignores underdetermination.[9]

Versus other research models edit

The hypothetico-deductive approach contrasts with other research models such as the inductive approach or grounded theory. In the data percolation methodology, the hypothetico-deductive approach is included in a paradigm of pragmatism by which four types of relations between the variables can exist: descriptive, of influence, longitudinal or causal. The variables are classified in two groups, structural and functional, a classification that drives the formulation of hypotheses and the statistical tests to be performed on the data so as to increase the efficiency of the research. [10]

See also edit

Types of inference edit

Citations edit

  1. ^ Popper, Karl (1959). The Logic of Scientific Discovery. Abingdon-on-Thames: Routledge.
  2. ^ Peter Godfrey-Smith (2003) Theory and Reality, p. 236.
  3. ^ Taleb 2007 e.g., p. 58, devotes his chapter 5 to the error of confirmation.
  4. ^ "I believe that we do not know anything for certain, but everything probably." —Christiaan Huygens, Letter to Pierre Perrault, 'Sur la préface de M. Perrault de son traité del'Origine des fontaines' [1763], Oeuvres Complétes de Christiaan Huygens (1897), Vol. 7, 298. Quoted in Jacques Roger, The Life Sciences in Eighteenth-Century French Thought, ed. Keith R. Benson and trans. Robert Ellrich (1997), 163. Quotation selected by Bynum & Porter 2005, p. 317 Huygens 317#4.
  5. ^ As noted by Alice Calaprice (ed. 2005) The New Quotable Einstein Princeton University Press and Hebrew University of Jerusalem, ISBN 0-691-12074-9 p. 291. Calaprice denotes this not as an exact quotation, but as a paraphrase of a translation of A. Einstein's "Induction and Deduction". Collected Papers of Albert Einstein 7 Document 28. Volume 7 is The Berlin Years: Writings, 1918-1921. A. Einstein; M. Janssen, R. Schulmann, et al., eds.
  6. ^ Murzi, Mauro (2001, 2008), "Carl Gustav Hempel (1905—1997)", Internet Encyclopedia of Philosophy. Murzi used the term relative frequency rather than probability.
  7. ^ John W. N. Watkins (1984), Science and Skepticism, p. 319.
  8. ^ Karl R. Popper (1979, Rev. ed.), Objective Knowledge, pp. 30, 360.
  9. ^ Sean Carroll. "What is Science?".
  10. ^ Mesly, Olivier (2015), Creating Models in Psychological Research, United States: Springer Psychology, p. 126, ISBN 978-3-319-15752-8

References edit

April 26, 2024
hypothetico, deductive, model, hypothetico, deductive, model, method, proposed, description, scientific, method, according, scientific, inquiry, proceeds, formulating, hypothesis, form, that, falsifiable, using, test, observable, data, where, outcome, known, t. The hypothetico deductive model or method is a proposed description of the scientific method According to it scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable using a test on observable data where the outcome is not yet known A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis A test outcome that could have but does not run contrary to the hypothesis corroborates the theory It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions 1 Contents 1 Example 2 Discussion 2 1 Versus other research models 3 See also 3 1 Types of inference 4 Citations 5 ReferencesExample editMain article Scientific method One example of an algorithmic statement of the hypothetico deductive method is as follows 2 1 Use your experience Consider the problem and try to make sense of it Gather data and look for previous explanations If this is a new problem to you then move to step 2 2 Form a conjecture hypothesis When nothing else is yet known try to state an explanation to someone else or to your notebook 3 Deduce predictions from the hypothesis if you assume 2 is true what consequences follow 4 Test or experiment Look for evidence observations that conflict with these predictions in order to disprove 2 It is a fallacy or error in one s reasoning to seek 3 directly as proof of 2 This formal fallacy is called affirming the consequent 3 One possible sequence in this model would be 1 2 3 4 If the outcome of 4 holds and 3 is not yet disproven you may continue with 3 4 1 and so forth but if the outcome of 4 shows 3 to be false you will have to go back to 2 and try to invent a new 2 deduce a new 3 look for 4 and so forth Note that this method can never absolutely verify prove the truth of 2 It can only falsify 2 4 This is what Einstein meant when he said No amount of experimentation can ever prove me right a single experiment can prove me wrong 5 Discussion editAdditionally as pointed out by Carl Hempel 1905 1997 this simple view of the scientific method is incomplete a conjecture can also incorporate probabilities e g the drug is effective about 70 of the time 6 Tests in this case must be repeated to substantiate the conjecture in particular the probabilities In this and other cases we can quantify a probability for our confidence in the conjecture itself and then apply a Bayesian analysis with each experimental result shifting the probability either up or down Bayes theorem shows that the probability will never reach exactly 0 or 100 no absolute certainty in either direction but it can still get very close to either extreme See also confirmation holism Qualification of corroborating evidence is sometimes raised as philosophically problematic The raven paradox is a famous example The hypothesis that all ravens are black would appear to be corroborated by observations of only black ravens However all ravens are black is logically equivalent to all non black things are non ravens this is the contrapositive form of the original implication This is a green tree is an observation of a non black thing that is a non raven and therefore corroborates all non black things are non ravens It appears to follow that the observation this is a green tree is corroborating evidence for the hypothesis all ravens are black Attempted resolutions may distinguish non falsifying observations as to strong moderate or weak corroborations investigations that do or do not provide a potentially falsifying test of the hypothesis 7 Evidence contrary to a hypothesis is itself philosophically problematic Such evidence is called a falsification of the hypothesis However under the theory of confirmation holism it is always possible to save a given hypothesis from falsification This is so because any falsifying observation is embedded in a theoretical background which can be modified in order to save the hypothesis Karl Popper acknowledged this but maintained that a critical approach respecting methodological rules that avoided such immunizing stratagems is conducive to the progress of science 8 Physicist Sean Carroll claims the model ignores underdetermination 9 Versus other research models edit The hypothetico deductive approach contrasts with other research models such as the inductive approach or grounded theory In the data percolation methodology the hypothetico deductive approach is included in a paradigm of pragmatism by which four types of relations between the variables can exist descriptive of influence longitudinal or causal The variables are classified in two groups structural and functional a classification that drives the formulation of hypotheses and the statistical tests to be performed on the data so as to increase the efficiency of the research 10 See also editConfirmation bias Deductive nomological Explanandum and explanans Inquiry Models of scientific inquiry Philosophy of science Pragmatism Scientific method Verifiability theory of meaning Will to believe doctrine Types of inference edit Strong inference Abductive reasoning Deductive reasoning Inductive reasoning AnalogyCitations edit Popper Karl 1959 The Logic of Scientific Discovery Abingdon on Thames Routledge Peter Godfrey Smith 2003 Theory and Reality p 236 Taleb 2007 e g p 58 devotes his chapter 5 to the error of confirmation I believe that we do not know anything for certain but everything probably Christiaan Huygens Letter to Pierre Perrault Sur la preface de M Perrault de son traite del Origine des fontaines 1763 Oeuvres Completes de Christiaan Huygens 1897 Vol 7 298 Quoted in Jacques Roger The Life Sciences in Eighteenth Century French Thought ed Keith R Benson and trans Robert Ellrich 1997 163 Quotation selected by Bynum amp Porter 2005 p 317 Huygens 317 4 As noted by Alice Calaprice ed 2005 The New Quotable Einstein Princeton University Press and Hebrew University of Jerusalem ISBN 0 691 12074 9 p 291 Calaprice denotes this not as an exact quotation but as a paraphrase of a translation of A Einstein s Induction and Deduction Collected Papers of Albert Einstein 7 Document 28 Volume 7 is The Berlin Years Writings 1918 1921 A Einstein M Janssen R Schulmann et al eds Murzi Mauro 2001 2008 Carl Gustav Hempel 1905 1997 Internet Encyclopedia of Philosophy Murzi used the term relative frequency rather than probability John W N Watkins 1984 Science and Skepticism p 319 Karl R Popper 1979 Rev ed Objective Knowledge pp 30 360 Sean Carroll What is Science Mesly Olivier 2015 Creating Models in Psychological Research United States Springer Psychology p 126 ISBN 978 3 319 15752 8References editBrody Thomas A 1993 The Philosophy Behind Physics Springer Verlag ISBN 0 387 55914 0 Luis de la Pena and Peter E Hodgson eds Bynum W F Porter Roy 2005 Oxford Dictionary of Scientific Quotations Oxford ISBN 0 19 858409 1 Godfrey Smith Peter 2003 Theory and Reality An introduction to the philosophy of science University of Chicago Press ISBN 0 226 30063 3 Taleb Nassim Nicholas 2007 The Black Swan Random House ISBN 978 1 4000 6351 2 Retrieved from https en wikipedia org w index php title Hypothetico deductive model amp oldid 1208442361, wikipedia, wiki, book, books, library,

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